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Neuro-Glial Interaction: SONG-Net

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Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9491))

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Abstract

More convincing evidence has proven the existence of a bidirectional relationship between neurons and astrocytes. Astrocytes, a new type of glial cells previously considered as passive support cells, constitute a system of non-synaptic transmission playing a major role in modulating the activity of neurons. In this context, this paper proposes to model the effect of these cells to develop a new type of artificial neural network operating on new mechanisms to improve the information processing and reduce learning time, very expensive in traditional networks. The obtained results indicate that the implementation of bio-inspired functions such as of astrocytes, improve very considerably learning speed.

The developed model achieves learning up to twelve times faster than traditional artificial neural networks.

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Author information

Authors and Affiliations

  1. Informatics for Industrial Systems Laboratory, University of Carthage, Carthage, Tunisia

    Kirmene Marzouki

  2. Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia

    Kirmene Marzouki

Authors
  1. Kirmene Marzouki
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Correspondence to Kirmene Marzouki .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

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© 2015 Springer International Publishing Switzerland

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Marzouki, K. (2015). Neuro-Glial Interaction: SONG-Net. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_70

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  • DOI: https://doi.org/10.1007/978-3-319-26555-1_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26554-4

  • Online ISBN: 978-3-319-26555-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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